A detailed summary statistics that provides the basis for the numbers in the paper. This analysis ensures the reproducibility of the pipeline and creates the statistics from raw data.
R version and package dependencies that were used R version 3.3.3 (2017-03-06) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.2 LTS
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=nl_NL.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=nl_NL.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=nl_NL.UTF-8 LC_NAME=nl_NL.UTF-8 LC_ADDRESS=nl_NL.UTF-8 LC_TELEPHONE=nl_NL.UTF-8 LC_MEASUREMENT=nl_NL.UTF-8 LC_IDENTIFICATION=nl_NL.UTF-8
attached base packages: [1] grid stats graphics grDevices utils datasets methods base
other attached packages: [1] markdown_0.7.7 cowplot_0.7.0 NMF_0.20.6 cluster_2.0.6 rngtools_1.2.4 pkgmaker_0.22 registry_0.3 heatmap3_1.1.1 sm_2.2-5.4 corrplot_0.77 prettyR_2.2 reshape_0.8.6 XLConnect_0.2-12 XLConnectJars_0.2-12 readr_1.1.0 qdap_2.2.5 qdapTools_1.3.1 qdapRegex_0.7.2 qdapDictionaries_1.0.6 gridExtra_2.2.1 reshape2_1.4.2 stringr_1.2.0 mvtnorm_1.0-6 plyr_1.8.4 RColorBrewer_1.1-2
[26] ggplot2_2.2.1 drc_3.0-1 MASS_7.3-45
loaded via a namespace (and not attached): [1] nlme_3.1-131 bitops_1.0-6 pbkrtest_0.4-7 doParallel_1.0.10 tools_3.3.3 R6_2.2.0 DBI_0.6-1 lazyeval_0.2.0 mgcv_1.8-16 colorspace_1.3-2 openNLPdata_1.5.3-2 nnet_7.3-12 chron_2.3-50 quantreg_5.29 reports_0.1.4 SparseM_1.76 NLP_0.1-10 sandwich_2.3-4 labeling_0.3 slam_0.1-40 scales_0.4.1 tm_0.7-1 digest_0.6.12 minqa_1.2.4 lme4_1.1-12 plotrix_3.6-4 zoo_1.7-14 openNLP_0.2-6 gtools_3.5.0
[30] dplyr_0.5.0 xlsx_0.5.7 car_2.1-4 RCurl_1.95-4.8 magrittr_1.5 wordcloud_2.5 Matrix_1.2-8 Rcpp_0.12.10 munsell_0.4.3 stringi_1.1.5 multcomp_1.4-6 parallel_3.3.3 gdata_2.17.0 gender_0.5.1 lattice_0.20-35 splines_3.3.3 xlsxjars_0.6.1 hms_0.3 venneuler_1.1-0 igraph_1.0.1 fastcluster_1.1.22 codetools_0.2-15 XML_3.98-1.6 data.table_1.10.4 nloptr_1.0.4 foreach_1.4.3 MatrixModels_0.4-1 gtable_0.2.0 assertthat_0.1
[59] gridBase_0.4-7 xtable_1.8-2 survival_2.41-3 tibble_1.3.0 rJava_0.9-8 iterators_1.0.8 TH.data_1.0-8
Number of unique strains: 124
Table containing the number of strains and antimicrobials
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| training | 84 | 84 | 84 | 84 | 84 | 84 | 84 | 588 |
| validation | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 280 |
| Sum | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 868 |
Samples that are above or below limit of detection new method (split according to antimicrobials)
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| above limit of detection | 4 | 0 | 0 | 0 | 7 | 4 | 0 | 15 |
| quality ok | 120 | 124 | 124 | 124 | 117 | 120 | 124 | 853 |
| Sum | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 868 |
Limit of detection (excluding data with Etest above limit of detection, if dose response does not result in limit of detection)
| training | validation | Sum | |
|---|---|---|---|
| above limit of detection | 6 | 9 | 15 |
| quality ok | 582 | 271 | 853 |
| Sum | 588 | 280 | 868 |
Summary of samples that are above limit of detection new method (all samples summarized, limit of detection includes also Etest limit of detection)
| training | validation | Sum | |
|---|---|---|---|
| limit of detection | 17 | 14 | 31 |
| quality ok | 571 | 266 | 837 |
| Sum | 588 | 280 | 868 |
Dose response curves for all antimicrobials (training and validation data):
Figure 1. Potency shift of antimicrobials across different strains of N. gonorrhoeae. Dose response curves for all strains and antimicrobials are shown (except samples above limit of detection). Strains that were classified as susceptible according to EUCAST 2016 MIC breakpoints were coloured in green, intermediate resistant strains in blue and resistant strains in red. The gradual shift of the potencies (EC50) towards higher concentrations can be observed for all antimicrobials.
## [[1]]
## [[1]]$Estimates
##
## Call:
## lm(formula = log(etest) ~ (esti))
##
## Coefficients:
## (Intercept) esti
## 1.101 1.001
##
##
## [[1]]$Matrix
## [[1]]$Matrix[[1]]
## (Intercept) esti
## (Intercept) 0.0023234883 0.0003695523
## esti 0.0003695523 0.0002613105
##
## [[1]]$Matrix$Summary
##
## Call:
## lm(formula = log(etest) ~ (esti))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1999 -0.6237 0.0616 0.7230 3.0847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.10145 0.04820 22.85 <2e-16 ***
## esti 1.00057 0.01617 61.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 569 degrees of freedom
## Multiple R-squared: 0.8707, Adjusted R-squared: 0.8705
## F-statistic: 3831 on 1 and 569 DF, p-value: < 2.2e-16
[1] 0.9331071
Outliers were defined as higher than +/- four doubling dilutions different from Etest MIC. The column “deviation” displays doubling dilutions deviation of predicted values from MIC. The column “compare” displays the EUCAST to the predicted classification.
| ID | strain | antibiotic | MIC | Etest_predicted | deviation | compare |
|---|---|---|---|---|---|---|
| 11_Ceftriaxone_40strains2.txt | Fluorometric11 | Ceftriaxone | 0.002 | 0.4702 | 7.877 | S_to_R |
| 14_Cefixime_40strains2.txt | Fluorometric14 | Cefixime | 0.016 | 1.083 | 6.081 | S_to_R |
| 17_Ceftriaxone_40strains2.txt | Fluorometric17 | Ceftriaxone | 0.004 | 2.387 | 9.221 | S_to_R |
| 18_Cefixime_40strains2.txt | Fluorometric18 | Cefixime | 0.016 | 9.451 | 9.206 | S_to_R |
| 18_Ceftriaxone_40strains2.txt | Fluorometric18 | Ceftriaxone | 0.002 | 0.4898 | 7.936 | S_to_R |
| 18_Penicillin_80strains3.txt | Fluorometric18 | Penicillin | 32 | 1.923 | -4.057 | R_to_R |
| 1_Ceftriaxone_40strains1.txt | Fluorometric1 | Ceftriaxone | 0.002 | 0.3237 | 7.339 | S_to_R |
| 33_Ceftriaxone_80strains5.txt | Fluorometric33 | Ceftriaxone | 0.004 | 0.1736 | 5.44 | S_to_R |
| 34_Ceftriaxone_80strains5.txt | Fluorometric34 | Ceftriaxone | 0.004 | 0.06407 | 4.002 | S_to_S |
| 37_Ceftriaxone_80strains5.txt | Fluorometric37 | Ceftriaxone | 0.008 | 0.2569 | 5.005 | S_to_R |
| 4_Penicillin_40strains1.txt | Fluorometric4 | Penicillin | 3 | 118.8 | 5.307 | R_to_R |
| 4_Penicillin_80strains1.txt | Fluorometric4 | Penicillin | 4 | 78.77 | 4.3 | R_to_R |
| 57_Cefixime_80strains11.txt | Fluorometric57 | Cefixime | 0.016 | 0.3266 | 4.352 | S_to_R |
| 5_Penicillin_40strains1.txt | Fluorometric5 | Penicillin | 3 | 91.36 | 4.929 | R_to_R |
| 5_Spectinomycin_40strains1.txt | Fluorometric5 | Spectinomycin | 1024 | 25974 | 4.665 | R_to_R |
| 60_Cefixime_80strains8.txt | Fluorometric60 | Cefixime | 0.016 | 1.067 | 6.059 | S_to_R |
| 66_Ceftriaxone_80strains9.txt | Fluorometric66 | Ceftriaxone | 0.004 | 0.000183 | -4.45 | S_to_S |
| 73_Cefixime_80strains9.txt | Fluorometric73 | Cefixime | 0.016 | 0.2623 | 4.035 | S_to_R |
| 73_Ceftriaxone_80strains9.txt | Fluorometric73 | Ceftriaxone | 0.008 | 0.1709 | 4.417 | S_to_R |
Results from the regression analysis show that the correlation with Etest is excellent however all values are systematically shifted towards lower values.
Figure 2. Correlation and deviations between the Etest MICs and predicted MICs. (a) Linear regression between EC50 and Etest MIC for the training data (84 strains). The Pearson’s correlation coefficient for the linear regression (blue line) was 0.93 and the confidence interval highlighted in grey. Slope and intercept for a perfect correlation was drawn as dashed black line for comparison. (b) The kernel density function of the EC50 values for the training data (n=269) is shown in red (median -1.68). The kernel density of the predicted MICs for training and validation data (n=837) is shown in purple (median -0.015). (c) Deviations of predicted MICs from Etest MIC per antimicrobial (n=837). The boxplots show the median and 25%-75% quartiles. The whiskers span the range from the bottom 5% to the highest 95% of the data. The essential agreement (EA) is written below the boxplots.
Table containing the categories (assuming Etest MIC according to EUCAST categories as gold standard)
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| I | 38 | 0 | 0 | 0 | 37 | 0 | 25 | 100 |
| R | 60 | 35 | 10 | 87 | 82 | 7 | 96 | 377 |
| S | 26 | 89 | 114 | 37 | 5 | 117 | 3 | 391 |
| Sum | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 868 |
EUCAST as rows, predicted categories as columns
| S | I | R | Sum | |
|---|---|---|---|---|
| S | 307 | 8 | 76 | 391 |
| I | 13 | 42 | 45 | 100 |
| R | 1 | 12 | 364 | 377 |
| Sum | 321 | 62 | 485 | 868 |
| S | I | R | Sum | |
|---|---|---|---|---|
| S | 0.354 | 0.009 | 0.088 | 0.45 |
| I | 0.015 | 0.048 | 0.052 | 0.115 |
| R | 0.001 | 0.014 | 0.419 | 0.434 |
| Sum | 0.37 | 0.071 | 0.559 | 1 |
EUCAST_to_predicted_categories
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| I_to_I | 9 | 0 | 0 | 0 | 30 | 0 | 3 | 42 |
| I_to_R | 17 | 0 | 0 | 0 | 7 | 0 | 21 | 45 |
| I_to_S | 12 | 0 | 0 | 0 | 0 | 0 | 1 | 13 |
| R_to_I | 6 | 0 | 0 | 0 | 6 | 0 | 0 | 12 |
| R_to_R | 54 | 35 | 9 | 87 | 76 | 7 | 96 | 364 |
| R_to_S | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| S_to_I | 5 | 0 | 0 | 1 | 1 | 0 | 1 | 8 |
| S_to_R | 5 | 29 | 40 | 0 | 0 | 0 | 2 | 76 |
| S_to_S | 16 | 60 | 74 | 36 | 4 | 117 | 0 | 307 |
| Sum | 124 | 124 | 124 | 124 | 124 | 124 | 124 | 868 |
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| I_to_I | 0.01 | 0 | 0 | 0 | 0.035 | 0 | 0.003 | 0.048 |
| I_to_R | 0.02 | 0 | 0 | 0 | 0.008 | 0 | 0.024 | 0.052 |
| I_to_S | 0.014 | 0 | 0 | 0 | 0 | 0 | 0.001 | 0.015 |
| R_to_I | 0.007 | 0 | 0 | 0 | 0.007 | 0 | 0 | 0.014 |
| R_to_R | 0.062 | 0.04 | 0.01 | 0.1 | 0.088 | 0.008 | 0.111 | 0.419 |
| R_to_S | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0.001 |
| S_to_I | 0.006 | 0 | 0 | 0.001 | 0.001 | 0 | 0.001 | 0.009 |
| S_to_R | 0.006 | 0.033 | 0.046 | 0 | 0 | 0 | 0.002 | 0.088 |
| S_to_S | 0.018 | 0.069 | 0.085 | 0.041 | 0.005 | 0.135 | 0 | 0.354 |
| Sum | 0.143 | 0.143 | 0.143 | 0.143 | 0.143 | 0.143 | 0.143 | 1 |
| Azithromycin | Cefixime | Ceftriaxone | Ciprofloxacin | Penicillin | Spectinomycin | Tetracycline | Sum | |
|---|---|---|---|---|---|---|---|---|
| 0 | 91 | 93 | 88 | 114 | 87 | 110 | 116 | 699 |
| 1 | 29 | 19 | 36 | 6 | 30 | 10 | 8 | 138 |
| Sum | 120 | 112 | 124 | 120 | 117 | 120 | 124 | 837 |
Figure 3. Contingency table with categorical errors of model predicted MICs. Etest MIC data were classified into the categories resistant (R), susceptible (S) and intermediate resistant (I) according to the EUCAST 2016 criteria. The cutoff values (mg/L) are shown as dashed black lines. Predicted MIC values (n=868) are shown as point estimates (black dots) with 95% confidence interval (colored dashes). For some estimates no confidence interval could be calculated (limit of detection), those were drawn as triangles. Correctly classified strains are drawn in green. Minor errors resulting from misclassifications of intermediate strains are drawn in blue. Major errors (S to R) were found for ceftriaxone (n=40), cefixime (n=29), azithromycin (n=5) and tetracycline (n=2). One very major error (R to S) was found for ceftriaxone (red). A high number of estimates (n=140) has confidence intervals spanning two categories.
Note: I counted as R
EUCAST as rows, predicted categories as columns
| R | S | |
|---|---|---|
| R | 463 | 14 |
| S | 84 | 307 |
| R_to_R | R_to_S | S_to_R | S_to_S |
|---|---|---|---|
| 463 | 14 | 84 | 307 |
\(\frac{R-to-R}{R-to-R + R-to-S} =\) 0.9706
### 95% CI
binom.test(463, 477, p = 0.5,conf.level = 0.95)
##
## Exact binomial test
##
## data: 463 and 477
## number of successes = 463, number of trials = 477, p-value < 2.2e-16
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.9512455 0.9838631
## sample estimates:
## probability of success
## 0.9706499
\(\frac{S-to-S}{S-to-S + S-to-R} =\) 0.7852
### 95% CI
binom.test(307, 389, p = 0.5,conf.level = 0.95)
##
## Exact binomial test
##
## data: 307 and 389
## number of successes = 307, number of trials = 389, p-value < 2.2e-16
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.7452549 0.8286867
## sample estimates:
## probability of success
## 0.7892031
| Etest_deviation | AZM | CFM | CRO | CIP | PEN | SPT | TET |
|---|---|---|---|---|---|---|---|
| onedeviation | 53.3 | 30.4 | 52.4 | 57.5 | 60.7 | 57.5 | 58.1 |
| twodeviation | 95 | 67 | 79 | 93.3 | 82.9 | 89.2 | 93.5 |
| fourdeviation | 100 | 95.5 | 92.7 | 100 | 96.6 | 99.2 | 100 |
8 WHO reference strains were followed over a time-course of 0-15 hours and measured every 3 hours (one replicate)
Figure S1. Fluorescence based time-kill curves. Logarithmized fluorescence values are plotted against the time (h). Ten different dilutions of each antimicrobial, positive control (Inf) and negative control (conc. 0) were tested on eight WHO reference panel strains. Start concentrations were calibrated to approximately 107 CFU/ml which corresponds to a log fluorescence of 6. From 0-3 hours negative controls without antimicrobial resulted in decreased bacterial numbers, at 6 hours all samples show increased fluorescence.
8 Reference strains were used in this analysis. 3 replicates with seven antimicrobials (n=56) were used to calculate the coefficient of variation
| strain | antibiotic | MIC | mean | sd | CV |
|---|---|---|---|---|---|
| WHO F | Azithromycin | 0.125 | 0.01394 | 0.009803 | 0.7031 |
| WHO F | Cefixime | <0.016 | 0.0005485 | 4.743e-05 | 0.08647 |
| WHO F | Ceftriaxone | <0.002 | 0.0002095 | 0.0001515 | 0.7231 |
| WHO F | Ciprofloxacin | 0.004 | 0.002442 | 0.0003445 | 0.141 |
| WHO F | Penicillin | 0.032 | 0.005978 | 0.0005169 | 0.08648 |
| WHO F | Spectinomycin | 16 | 3.141 | 2.253 | 0.7174 |
| WHO F | Tetracycline | 0.25 | 0.04333 | 0.02345 | 0.5413 |
| WHO G | Azithromycin | 0.25 | 0.03174 | 0.0008395 | 0.02645 |
| WHO G | Cefixime | <0.016 | 0.003609 | 6.18e-05 | 0.01712 |
| WHO G | Ceftriaxone | 0.008 | 0.00183 | 0.000874 | 0.4776 |
| WHO G | Ciprofloxacin | 0.125 | 0.04593 | 0.003962 | 0.08626 |
| WHO G | Penicillin | 0.5 | 0.1495 | 0.03925 | 0.2625 |
| WHO G | Spectinomycin | 16 | 1.556 | 1.256 | 0.807 |
| WHO G | Tetracycline | 32 | 9.979 | 2.121 | 0.2126 |
| WHO K | Azithromycin | 0.25 | 0.04507 | 0.005532 | 0.1227 |
| WHO K | Cefixime | 0.25 | 0.1223 | 0.01594 | 0.1303 |
| WHO K | Ceftriaxone | 0.064 | 0.02189 | 0.01038 | 0.474 |
| WHO K | Ciprofloxacin | >32 | 9.845 | 1.578 | 0.1603 |
| WHO K | Penicillin | 2 | 0.7465 | 0.07489 | 0.1003 |
| WHO K | Spectinomycin | 16 | 2.669 | 1.447 | 0.542 |
| WHO K | Tetracycline | 2 | 1.368 | 0.1663 | 0.1215 |
| WHO L | Azithromycin | 0.5 | 0.04175 | 0.007214 | 0.1728 |
| WHO L | Cefixime | 0.125 | 0.06644 | 0.007194 | 0.1083 |
| WHO L | Ceftriaxone | 0.25 | 0.05039 | 0.02726 | 0.5409 |
| WHO L | Ciprofloxacin | >32 | 4.47 | 0.626 | 0.1401 |
| WHO L | Penicillin | 2 | 0.9015 | 0.0676 | 0.07498 |
| WHO L | Spectinomycin | 16 | 1.919 | 1.076 | 0.5605 |
| WHO L | Tetracycline | 2 | 1.187 | 0.2155 | 0.1815 |
| WHO M | Azithromycin | 0.25 | 0.0404 | 0.003365 | 0.08328 |
| WHO M | Cefixime | <0.016 | 0.00291 | 0.0003033 | 0.1042 |
| WHO M | Ceftriaxone | 0.012 | 0.001849 | 0.0008431 | 0.4558 |
| WHO M | Ciprofloxacin | 2 | 0.2936 | 0.03352 | 0.1142 |
| WHO M | Penicillin | 8 | 22.13 | 1.932 | 0.08727 |
| WHO M | Spectinomycin | 16 | 2.266 | 1.16 | 0.5119 |
| WHO M | Tetracycline | 2 | 0.9742 | 0.09473 | 0.09724 |
| WHO N | Azithromycin | 0.25 | 0.02093 | 0.00204 | 0.0975 |
| WHO N | Cefixime | <0.016 | 0.004648 | 0.0003874 | 0.08336 |
| WHO N | Ceftriaxone | 0.004 | 0.001196 | 0.0007731 | 0.6463 |
| WHO N | Ciprofloxacin | 4 | 1.588 | 0.03178 | 0.02001 |
| WHO N | Penicillin | 8 | 2.304 | 1.996 | 0.8663 |
| WHO N | Spectinomycin | 16 | 1.162 | 0.8212 | 0.7065 |
| WHO N | Tetracycline | 16 | 6.36 | 0.4289 | 0.06742 |
| WHO O | Azithromycin | 0.25 | 0.04498 | 0.007163 | 0.1592 |
| WHO O | Cefixime | 0.016 | 0.009882 | 0.004161 | 0.4211 |
| WHO O | Ceftriaxone | 0.032 | 0.004744 | 0.003545 | 0.7473 |
| WHO O | Ciprofloxacin | 0.008 | 0.002171 | 0.0003707 | 0.1707 |
| WHO O | Penicillin | >32 | 10.29 | 4.058 | 0.3944 |
| WHO O | Spectinomycin | >1024 | 462.4 | 167.8 | 0.3629 |
| WHO O | Tetracycline | 2 | 0.9739 | 0.1611 | 0.1655 |
| WHO P | Azithromycin | 4 | 0.2521 | 0.006027 | 0.0239 |
| WHO P | Cefixime | <0.016 | 0.003542 | 0.0006906 | 0.195 |
| WHO P | Ceftriaxone | 0.004 | 0.001126 | 0.0005669 | 0.5037 |
| WHO P | Ciprofloxacin | 0.004 | 0.001845 | 0.0001956 | 0.106 |
| WHO P | Penicillin | 0.25 | 0.09521 | 0.001573 | 0.01652 |
| WHO P | Spectinomycin | 8 | 2.168 | 1.272 | 0.5867 |
| WHO P | Tetracycline | 1 | 0.4477 | 0.1042 | 0.2327 |
print(summary(cv$CV))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01652 0.09743 0.16810 0.29190 0.50570 0.86630
Figure S2. Intra assay coefficient of variation. To test the reproducibility of the resazurin MIC assay seven antimicrobials were tested on eight WHO reference strains (n=56). The mean and standard deviation of three independent experiments was calculated. The coefficient of variation (ratio of standard deviation over the mean) was calculated for sample. Barplots are shown for each sample. The mean of the coefficient of variation (intra assay CV) is 0.29.
It was tested if the Hill coefficient was significantly different (t-test) between antimicrobials and strains.
| antibiotic | mean | sd | min | max | |
|---|---|---|---|---|---|
| 3 | Ceftriaxone | 1.6 | 1.3 | 0.1124 | 7.455 |
| 2 | Cefixime | 1.9 | 1.5 | 0.1095 | 7.151 |
| 7 | Tetracycline | 2.1 | 0.9 | 0.4026 | 7.818 |
| 5 | Penicillin | 2.5 | 1.7 | 0.3353 | 10.47 |
| 1 | Azithromycin | 2.6 | 1.5 | 0.6032 | 13.44 |
| 4 | Ciprofloxacin | 2.7 | 1.2 | 0.2596 | 7.733 |
| 6 | Spectinomycin | 2.9 | 1.7 | 0.3138 | 9.158 |
Figure S3. Difference of Hill coefficients. (a) The difference between the mean of 124 Hill coefficients (124 clinical strains examined) is shown for each antimicrobial combination. High values are shown in an increasingly intense blue colour gradient and low values in red. A pairwise t-test was performed and non-significant differences (p value > 0.05) marked with a black cross. (b) Hierarchical clustering of Hill coefficients. Rows represent Hill coefficients for different strains (N=124) and columns antimicrobials. The beta-lactams penicillin G, ceftriaxone and cefixime are more similar to each other than to the other antimicrobials.
The dose response curves with four parameters might not capture the effects for beta-lactams well. To demonstrate this one example is shown here in detail. A biphasic model fits this data better than the simpler four parameter function. However to fit this model without overparametrization, this requires dense dose spacing and at least three replicates per sample.
Figure S4. Biphasic dose response curves. The viability (%) was plotted against 24 different antimicrobial concentrations. Mean and standard error of three independent experiments are shown. (a) Ceftriaxone in Strain 11 (validation data). A biphasic model (red curve) fits the model better (bic=563) than a monophasic model (bic=794).1 The first EC50 is at 0.12 mg/L and the second at 1.21 mg/L (Etest MIC=0.125 mg/L). (b) Cefixime in Strain 11 (validation data). A biphasic model (red curve) fits the model better (bic=850) than a monophasic model (bic=8574). The first EC50 is at 0.16 mg/L and the second at 1.39 mg/L (Etest MIC=0.25 mg/L).